Three-dimensional (3-D) imaging and 3-D image processing are areas of growing interest to dental/orthodontic practitioners for computer-aided diagnosis and overall improved patient care. In the field of cephalometric analysis, 3-D imaging and 3-D image processing offer significant advantages in terms of flexibility, accuracy, and repeatability. 3-D cephalometric analysis overcomes some of the shortcomings associated with conventional methods of two-dimensional (2-D) cephalometric analysis, such as 2-D geometric errors of perspective projection, magnification, and head positioning in projection, for example. 3-D cephalometrics has been shown to yield objective data that is more accurate, since it is based on calculation rather than being largely dependent upon discrete measurements, as is the case with 2-D cephalometrics.
Early research using 3-D cephalometrics methods employed 3-D imaging and parametric analysis of maxillo-facial anatomical structures using cone beam computed tomography (CBCT) of a patient's head. Using CBCT methods, a significant role of the 3-D cephalometric analysis was to define mathematical models of maxillary and mandibular arches for which the axes of inertia were calculated for each tooth or group of teeth. This, in turn, required the segmentation of individual teeth from the acquired CBCT head volume of a patient.
Conventionally, during an orthodontic treatment procedure, multiple 2-D X-ray cephalogram acquisitions are used to assess treatment progress. Conventional 3-D cephalometric analysis can also be used for this purpose, requiring multiple CBCT scans. However, both 2-D and 3-D radiographic imaging methods expose the patient to ionizing radiation. Reducing overall patient exposure to radiation is desirable, particularly for younger patients.
Optical intraoral scans, in general, produce contours of dentition objects and have been helpful in improving visualization of teeth, gums, and other intra-oral structures. Surface contour information can be particularly useful for assessment of tooth condition and has recognized value for various types of dental procedures, such as for restorative dentistry. This can provide a valuable tool to assist the dental practitioner in identifying various problems and in validating other measurements and observations related to the patient's teeth and supporting structures. Surface contour information can also be used to generate 3-D models of dentition components such as individual teeth; the position and orientation information related to individual teeth can then be used in assessing orthodontic treatment progress. With proper use of surface contour imaging, the need for multiple 2-D or 3-D X-ray acquisitions of a patient's dentition can be avoided.
A number of techniques have been developed for obtaining surface contour information from various types of objects in medical, industrial, and other applications. Optical 3-dimensional (3-D) measurement methods provide shape and spatial information using light directed onto a surface in various ways. Among types of imaging methods used for contour imaging are fringe projection devices. Fringe projection imaging uses patterned or structured light and camera/sensor triangulation to obtain surface contour information for structures of various types. Once the fringe projection images are processed, a point cloud can be generated. A mesh can then be formed from the point cloud or a plurality of point clouds, in order to reconstruct at least a planar approximation to the surface.
Mesh representation can be particularly useful for showing surface structure of teeth and gums and can be obtained using a handheld camera and without requiring harmful radiation levels. However, when using conventional image processing approaches, mesh representation has been found to lack some of the inherent versatility and utility that is available using cone-beam computed tomography (CBCT) or other techniques that expose the patient to radiation. One area in which mesh representation has yielded only disappointing results relates to segmentation. Segmentation allows the practitioner to identify and isolate the crown and other visible portions of the tooth from gums and related supporting structure. Conventional methods for segmentation of mesh images can often be inaccurate and may fail to distinguish tooth structure from supporting tissues.
Various approaches for addressing the segmentation problem for mesh images have been proposed, such as the following:                (i) A method described in the article “Snake-Based Segmentation of Teeth from Virtual Dental Casts” by Thomas Kronfeld et al. (in Computer-Aided Design & applications, 7(a), 2010) employs an active contour segmentation method that attempts to separate every tooth and gum surface in a single processing iteration. The approach that is described, however, is not a topology-independent method and can fail, particularly where there are missing teeth in the jaw mesh.        (ii) An article entitled “Perception-based 3D Triangle Mesh Segmentation Using Fast Marching Watershed” by Page, D. L. et al. (in Proc. CVPI vol II 2003) describes using a Fast Marching Watershed method for mesh segmentation. The Fast Marching Watershed method that is described requires the user to manually enter seed points. The seed points must be placed at both sides of the contours of the regions under segmentation. The method then attempts to segment all regions in one step, using seed information. For jaw mesh segmentation, this type of method segments each tooth as well as the gum at the same time. This makes the method less desirable, because segmenting teeth from the gum region typically requires parameters and processing that differ from those needed for the task of segmenting teeth from each other. Using different segmentation strategies for different types of dentition components with alternate segmentation requirements would provide better performance.        (iii) For support of his thesis, “Evaluation of software developed for automated segmentation of digital dental models”, J. M. Moon used a software tool that decomposed the segmentation process into two steps: separation of teeth from gingival structure and segmentation of whole arch structure into individual tooth objects. The software tool used in Moon's thesis finds maximum curvature in the mesh and requires the user to manually choose a curvature threshold to obtain margin vertices that are used for segmenting the tooth. The software also requires the user to manually edit margins in order to remove erroneous segmentation results. Directed to analysis of shape and positional characteristics, this software tool does not consider employing color information in the separation of teeth regions from the gum regions.        (iv) U.S. Patent application 20030039389 A1 entitled “Manipulation a digital dentition model to form models of individual dentition components” by Jones, T. N. et al. disclose a method of separating portions of the dentition model representing the adjacent teeth.        
While conventional methods exhibit some level of success with a limited set of test cases, none of these methods appears to be robust and commercially viable. There is, then, a need for improved methods for segmentation of mesh representation of tooth and gum structures.